# pRSL: Interpretable Multi-label Stacking by Learning Probabilistic Rules

arXiv: https://arxiv.org/abs/2105.13850 · HTML: https://mlsanity.com/paper/2105.13850/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration](https://mlsanity.com/paper/1910.12656/) (2019)
- [Loopy Belief Propagation for Approximate Inference: An Empirical Study](https://mlsanity.com/paper/1301.6725/) (2013)
- [Multi-Label Zero-Shot Learning with Structured Knowledge Graphs](https://mlsanity.com/paper/1711.06526/) (2017)
- [Attribute Propagation Network for Graph Zero-shot Learning](https://mlsanity.com/paper/2009.11816/) (2020)
- [Multi-label Learning with Missing Labels using Mixed Dependency Graphs](https://mlsanity.com/paper/1804.00117/) (2018)
- [Learning Gradient Boosted Multi-label Classification Rules](https://mlsanity.com/paper/2006.13346/) (2020)
- [Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning](https://mlsanity.com/paper/1806.02664/) (2018)
- [Probabilistic Label Relation Graphs with Ising Models](https://mlsanity.com/paper/1503.01428/) (2015)
- [On MAP Inference by MWSS on Perfect Graphs](https://mlsanity.com/paper/1309.6872/) (2013)

## Built on by (most cited first, 1 of 1)

- [Applications of human activity recognition in industrial processes -- Synergy of human and technology](https://mlsanity.com/paper/2212.02266/) (2022)

## Abstract (arXiv)

A key task in multi-label classification is modeling the structure between the involved classes. Modeling this structure by probabilistic and interpretable means enables application in a broad variety of tasks such as zero-shot learning or learning from incomplete data. In this paper, we present the probabilistic rule stacking learner (pRSL) which uses probabilistic propositional logic rules and belief propagation to combine the predictions of several underlying classifiers. We derive algorithms for exact and approximate inference and learning, and show that pRSL reaches state-of-the-art performance on various benchmark datasets. In the process, we introduce a novel multicategorical generalization of the noisy-or gate. Additionally, we report simulation results on the quality of loopy belief propagation algorithms for approximate inference in bipartite noisy-or networks.